According to Google’s 2023 travel trends, Vietnam consistently ranks among the most-searched destinations globally, reflecting a strong recovery in its tourism economy. A survey by ezCloud.vn in 2023 highlights that travelers increasingly prioritize exploratory and relaxing experiences. While online platforms have significantly enhanced the convenience of travel recommendations, they often lack the depth of personalization required to act as intelligent consultants who deeply understand travelers’ preferences. This research introduces a context-aware, personalized travel recommendation system leveraging state-of-the-art natural language processing techniques, including PhoBERT for Vietnamese text classification. Our system integrates historical user behavior, real-time contextual information such as weather and seasonal conditions, and semantic insights derived from user queries. Key components include a fast Tag-Key model for extracting relevant tags from user questions, a dynamic weighted dataset for customizable recommendations, and an architecture designed to harmonize historical and contextual inputs. With this approach, we achieved a satisfaction level of 84.52% in recommendations, showcasing the potential of NLP-driven tourism solutions.